Neural network for processing aptamer data

ABSTRACT

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for obtaining data defining a sequence for an aptamer, the aptamer comprising a string of nucleobases; encoding the data defining the sequence for the aptamer as a neural network input; and processing the neural network input using a neural network to generate an output that characterizes how strongly the aptamer binds to a particular target molecule, wherein the neural network has been configured through training to receive the data defining the sequence and to process the data to generate predicted outputs that characterize how strongly the aptamer binds to the particular target molecule.

CROSS-REFERENCE TO RELATED APPLICATIONS

This is a continuation of U.S. application Ser. No. 16/752,513, filed onJan. 24, 2020, which is a continuation of U.S. application Ser. No.14/921,973, filed on Oct. 23, 2015 (now U.S. Pat. No. 10,546,650). Thedisclosures of the prior applications are considered part of and areincorporated by reference in the disclosure of this application.

BACKGROUND

This specification relates to a neural network for processing aptamerdata.

Neural networks are machine learning models that employ one or morelayers of models to generate an output, e.g., one or moreclassifications, for a received input. Some neural networks include oneor more hidden layers in addition to an output layer. The output of eachhidden layer is used as input to the next layer in the network, i.e.,the next hidden layer or the output layer of the network. Each layer ofthe network generates an output from a received input in accordance withcurrent values of a respective set of parameters.

SUMMARY

In general, this specification describes a neural network for processingaptamer data.

In general, one innovative aspect of the subject matter described inthis specification can be embodied in methods that include the actionsof obtaining data defining a sequence for an aptamer, the aptamercomprising a string of nucleobases; encoding the data defining thesequence for the aptamer as a neural network input; and processing theneural network input using a neural network to generate an output thatcharacterizes how strongly the aptamer binds to a particular targetmolecule, wherein the neural network has been configured throughtraining to receive the data defining the sequence and to process thedata to generate predicted outputs that characterize how strongly theaptamer binds to the particular target molecule.

Implementations can include one or more of the following features. Theoutput that characterizes how strongly the aptamer binds comprises aplurality of SELEX round outputs, each SELEX round output correspondingto a respective round in an iterative SELEX process, wherein each SELEXround output characterizes how strongly the aptamer binds to theparticular target molecule for the corresponding SELEX round. The SELEXround output corresponding to an earlier SELEX round in the iterativeSELEX process is a binary value indicating whether the aptamer exists ina pool for use in a subsequent SELEX round. A SELEX round output for alater SELEX round in the SELEX process comprises a high throughputsequencing count of the aptamer, the high throughput sequencing countindicating how strongly the aptamer binds to the particular targetmolecule. The output that characterizes how strongly the aptamer bindscomprises a vector of fluorescence levels, where a brightness of eachfluorescence level indicates how strongly the aptamer binds to theparticular target molecule. The neural network is a convolutional neuralnetwork. The data defining the sequence comprises data specifyingsecondary structure of the aptamer. The data specifying the secondarystructure comprises data specifying a pattern of hydrogen bonds in theaptamer or an energy dot plot matrix of the aptamer. Encoding the datadefining the sequence comprises converting the data defining thesequence to a plurality of one-hot vectors, each one-hot vectorcorresponding to a respective nucleobase in the aptamer. Iterativelyapplying mutations to the sequence to generate a mutated sequence thathas a maximum strength of binding to the particular target molecule,comprising for each of a plurality of iterations: applying a mutation toa current mutated sequence to generate a new mutated sequence;processing the new mutated sequence using the neural network to generatean output that characterizes how strongly the new mutated sequence bindsto a particular target molecule; using the neural network to determinewhether the output indicates that the new mutated sequence bindsstronger than the current mutated sequence; and selecting anothermutation to be applied in a next iteration based on whether the outputindicates that the new mutated sequence binds stronger than the currentmutated sequence.

Another innovative aspect includes obtaining data defining a pluralityof sequences for aptamers, each aptamer comprising a string ofnucleobases, each sequence having a respective label comprisingcharacteristics of how strongly the respective aptamer binds to aparticular target molecule; encoding the data to generate training datafor a neural network; and training the neural network on the trainingdata, the neural network configured to output characteristics of howstrongly a particular input sequence binds to the particular targetmolecule.

Implementations can include one or more of the following features. Theneural network is configured to output a plurality of SELEX roundoutputs, each SELEX round output corresponding to a respective round inan iterative SELEX process, wherein each SELEX round outputcharacterizes how strongly an input aptamer binds to the particulartarget molecule for the corresponding SELEX round. Each label for arespective aptamer in the training data further comprises, a pluralityof binary values for the respective aptamer for a respective pluralityof SELEX rounds, each binary value for the respective SELEX roundindicating whether the aptamer exists in a pool for use in a subsequentSELEX round, and wherein the neural network output further comprises aplurality of binary values for a respective plurality of SELEX rounds,each binary value for the respective SELEX round indicating whether theaptamer exists in a pool for use in a subsequent SELEX round. Each labelfor a respective aptamer in the training data further comprises, aplurality of high throughput sequencing counts of the respective aptamerfor a respective plurality of SELEX rounds, and wherein the neuralnetwork output further comprises a plurality of high throughputsequencing counts of the aptamer, each high throughput sequencing countindicating how strongly the aptamer binds to the particular targetmolecule. One or more labels for a respective aptamer in the trainingdata further comprises, a vector of fluorescence levels for therespective aptamer, and wherein the neural network output furthercomprises a vector of fluorescence levels, where a brightness of eachfluorescence level indicates how strongly the aptamer binds to theparticular target molecule. The data defining the plurality of sequencesfor aptamers comprises, for each aptamer, data specifying secondarystructure of the aptamer. The data specifying the secondary structurecomprises data specifying a pattern of hydrogen bonds in the aptamer oran energy dot plot matrix of the aptamer.

Particular embodiments of the subject matter described in thisspecification can be implemented so as to realize one or more of thefollowing advantages. Conventional methods of identifying bindingstrength between aptamers and a particular target molecule includeapplying a SELEX process to randomly selected aptamers, which can belabor-intensive and frequently fails to yield an aptamer with strongspecific binding. Advantageously, a neural network can predict a bindingstrength for an input aptamer to a particular target molecule. Inparticular, the neural network can predict the SELEX round outputs fornovel sequences not found in the original random pool. Therefore,instead of requiring the best aptamer to be present in the initial,relatively small, random pool, scientists can predict the aptamer havingthe strongest specific binding in the set of all possible sequences.Thus, rather than performing multiple SELEX rounds that may or may notyield an aptamer with strong specific binding, the neural networkpredicts aptamers with strong specific bindings, which reduces cost andtesting time. The neural network can also model binding to multipletargets at once, and therefore identify an aptamer that likely binds tothe target molecule and not bind to known background molecules. Thedetails of one or more embodiments of the subject matter of thisspecification are set forth in the accompanying drawings and thedescription below. Other features, aspects, and advantages of thesubject matter will become apparent from the description, the drawings,and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example system for processing aptamer data.

FIG. 2 is a flow diagram of an example method for training a neuralnetwork on aptamer data.

FIG. 3 is a flow diagram of an example method for processing aptamerdata using a neural network.

FIG. 4 is a flow diagram of an example process for identifying newmutations to aptamers through an iterative process using a neuralnetwork.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

FIG. 1 illustrates an example aptamer processing system 100. The system100 is an example of a system implemented as computer programs on one ormore computers in one or more locations, in which the systems,components, and techniques described below can be implemented.

The system 100 includes a neural network 104. In some implementations,the neural network 104 is a convolutional neural network. The neuralnetwork 104 receives aptamer data 102. The aptamer data 102 can be datadefining a sequence for an aptamer. An aptamer is a single-strandedmolecule that can bind to particular target molecules including proteinsand peptides with a degree of affinity, i.e., strength.

The aptamer data can include a sequence of nucleotide bases for anaptamer. In some implementations, the sequence of nucleotide bases isencoded as a sequence of one-hot vectors. For example, a cytosinemolecule in the sequence can be represented by a vector [1 0 0 0] whilea thymine molecule can be represented by a vector [0 1 0 0].

The vectors can be appended to each other to form a matrix of values,e.g., represented as an image, which can be provided as a tensor inputto the neural network 104. For example, if the sequence of nucleotidebases is CTCT, the matrix of values can be the one-hot vectors of therespective nucleotide bases appended together to form the matrix:

[1 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0].

By way of illustration, in this example the matrix can be represented asa 4×4 image, where the 1's in the matrix are represented as black pixelsand the 0's in the matrix are represented as white pixels. The imagerepresenting the matrix can be provided as input to the neural network104. In some implementations, the neural network 104 performsconvolutions on the 4×4 image matrix in a horizontal direction and not avertical direction. This enables predictions to be based on whichnucleotide bases are sequentially adjacent to each other and not basedon how one-hot encodings of the nucleotides are implemented. In someother implementations, the matrix is provided as a sequence of bits tothe neural network.

The aptamer data can also include data characterizing the secondarystructure of the aptamer. For example, the data can specify a pattern ofhydrogen bonds in the aptamer or an energy dot plot matrix of theaptamer. Characteristics of the secondary structure can be representedas a byte sequence, e.g., as a one-hot vector or a matrix of values, andprovided to the neural network 104. In some implementations, the bytesequence of the secondary structure is appended to the matrix of valuesformed from the sequence of nucleotide bases to generate the aptamerdata that is provided as input to the neural network 104.

The neural network 104 can process the aptamer data 102 to generate anoutput 106. The output 106 characterizes how strongly the aptamer bindsto a particular target molecule. In particular, the output 106 cancorrespond to outputs generated from a SELEX process for the aptamerdata 102.

The SELEX process, i.e., systematic evolution of ligands by exponentialenrichment (SELEX), identifies binding strengths to a particular targetmolecule from a pool of aptamers. In particular, the SELEX processstarts with an initial pool of aptamers and processes the pool ofaptamers through multiple SELEX rounds. Each SELEX round selects a poolof aptamers that bind the target molecule and discards aptamers that donot bind to the target molecule. After each SELEX round, the resultingpool is amplified using polymerase chain reaction (PCR). During PCR,aptamers with stronger binding strengths tend to be replicated morefrequently than aptamers with weaker binding strengths.

After numerous SELEX rounds, a composition of the pool of aptamersindicates a convergence to a small number, e.g., on the order ofthousands, of aptamers with high counts, which can potentially indicatea strong binding strength to a target molecule.

By way of illustration, for a SELEX process, the initial pool can have asize on the order of 10¹³ aptamers. The initial pool can be referred toas the library of aptamers. The pool can be composed of nearly entirelyunique aptamers. After a first SELEX round, i.e., after discardingaptamers that do not bind to a target molecule and enriching aptamersthat do bind, the pool can be 99.3% unique. After a second SELEX round,the pool can be 90% unique and have a size on the order of 10⁸ aptamers.After another SELEX round, the pool can be less than 10% unique and havea size on the order of 10⁶ aptamers. After yet another SELEX round, thepool can be less than 5% unique and have a size on the order of 10⁵aptamers. After each SELEX round, the pool can undergo high throughputsequencing to identify aptamers in the SELEX round having high counts.

As described above, the output 106 corresponds to the outputs from theSELEX process for the aptamer data 102. In other words, the neuralnetwork 104 predicts, after each SELEX round, how strongly the inputaptamer in the aptamer data 102 binds to the target molecule.

Thus, the output 106 can include a library output 108. In someimplementations, the library output 108 is a binary value, e.g., true orfalse, of whether the input aptamer exists in the initial pool ofaptamers. In some other implementations, the library output 108 is aconfidence value between 0 and 1 that represents a likelihood that theinput aptamer exists in the initial pool of aptamers.

The output 106 can include SELEX 1 output 110. Selex 1 output 110 is theoutput after performing a first SELEX round in the SELEX process. Selex1 output 110 can also be a confidence value or a binary value of whetherthe input aptamer exists in the pool of aptamers after the first round.In some implementations, the neural network 104 generates binary orconfidence values for SELEX outputs that are in pools with a percentageof unique aptamers over a particular threshold. This is because a poolof aptamers with a high unique aptamer percentage generally does nothave high aptamer count for one particular aptamer, and thus does notindicate any particular aptamer with a high binding strength to thetarget molecule.

The output 106 can include a SELEX 2 output and a SELEX 3 output 112,114. Selex 2 and 3 output 112, 114 are outputs after performing a secondand third SELEX round, respectively. The output for a respective one ofthese SELEX rounds can be a predicted count of a number of inputaptamers in the respective SELEX round.

In some implementations, the neural network 104 generates, in the output106, an array or vector of fluorescent values 116. The fluorescentvalues represent a brightness, which indicates how strongly the inputaptamer binds to the target molecule as measured in a binding array.

To generate the output 106, the system 100 can train the neural network101 using conventional neural network training techniques, which will bedescribed further below with reference to FIG. 2 .

FIG. 2 is a flow diagram of an example process 200 for training a neuralnetwork on aptamer data. For convenience, the process 200 will bedescribed with respect to a system, e.g., the system 100 of FIG. 1 ,having one or more computing devices that execute software to implementthe process 200.

The system obtains data defining multiple sequences for aptamers (step202). Each sequence can be represented as multiple one-hot vectors, asdescribed above with reference to FIG. 1 .

Each sequence can also have a respective label that includescharacteristics of how strongly the respective aptamer binds to aparticular target molecule. A label can have the data specified by theoutput 106 of FIG. 1 .

In particular, the label can include binary values for the respectiveaptamer for earlier SELEX rounds. The binary values can indicate whetherthe aptamer exists in a pool of aptamers for the round, and thus,whether the aptamer exists in a pool for use in a subsequent SELEXround. The label can also include high throughput sequencing counts ofthe respective aptamer for later SELEX rounds. In some implementations,the labels also include data of vectors of fluorescence levels for theaptamer. These vectors of fluorescence levels can be obtained throughbinding array measurements. In some other implementations, the labelsinclude vectors of binding affinity values, where each binding affinityvalue corresponds to a likelihood of binding to a respective targetmolecule.

The system encodes the data to generate training data for a neuralnetwork (step 204). The system can encode the data defining the sequenceby converting the data to multiple one-hot vectors, as described abovewith reference to FIG. 1 .

The system trains the neural network on the training data (step 206).That is, the system processes the training data, e.g., the encodedsequence data, to generate predicted outputs. The system determines anerror between each predicted output and a corresponding portion of thelabel. The system uses the error to update values of the parameters usedin each layer of the neural network using conventional neural networktraining techniques, e.g., stochastic gradient descent withbackpropagation. After training, the neural network can generateoutputs, e.g., the output 106 of FIG. 1 , from input aptamer data.

FIG. 3 is a flow diagram of an example process 300 for processingaptamer data using a neural network. For convenience, the process 300will be described with respect to a system, e.g., the system 100 of FIG.1 , having one or more computing devices that execute software toimplement the process 300.

The system obtains data defining a sequence for an aptamer (step 302).Each sequence can be represented as multiple one-hot vectors, asdescribed above with reference to FIG. 1 . In some implementations, thedata is an image representing the sequence. The image can have black andwhite pixels as described above with reference to FIG. 1 . Additionally,in some implementations, the data also includes data encoding secondarystructure of the aptamer.

The system encodes the data defining the sequence as a neural networkinput (step 304). The system can encode the data defining the sequenceby converting the data to multiple one-hot vectors, as described abovewith reference to FIG. 1 .

The system processes the neural network input using a neural network togenerate an output that characterizes how strongly the aptamer binds toa particular target molecule (step 306). The neural network can betrained, as described above with reference to FIG. 2 . The output thatcharacterizes how strongly the aptamer binds to the particular targetmolecule can correspond to SELEX round outputs.

Each SELEX round output can correspond to a respective round in aniterative SELEX process, as described above with reference to FIG. 1 .That is, the system can generate an output including binary values for afirst few SELEX round outputs and numerical values for later SELEX roundoutputs. The binary values can indicate whether the aptamer exists in apool for use in subsequent SELEX rounds. The numerical values for thelater SELEX round outputs can be predicted counts, e.g., obtainedthrough high throughput sequencing, of the aptamer within a respectivepool in the respective SELEX round.

In some implementations, the output also includes an array output of avector of fluorescent values.

Although the output, e.g., the output 106 of FIG. 1 , indicates howstrongly an input aptamer binds to a particular target molecule, theremay be aptamers similar to the input aptamer that bind more strongly tothe particular target molecule. To identify these stronger aptamers,after generating the output, the system can iteratively apply mutationsto the sequence of an input aptamer to identify a mutated sequence thathas a maximum strength of binding to the particular target molecule.This iterative process can identify aptamers with high binding strengtheven if the aptamers were not in the initial pool during the SELEXprocess.

FIG. 4 is a flow diagram of an example process 400 for identifying newmutations to aptamers through an iterative process using a neuralnetwork. For convenience, the process 400 will be described with respectto a system, e.g., the system 100 of FIG. 1 , having one or morecomputing devices that execute software to implement the process 400.

For each iteration, the system applies a mutation to a current mutatedsequence, i.e., the sequence of the input aptamer in the firstiteration, to generate a new mutated sequence (step 402). The mutationcan randomly change a number of nucleotide bases in the sequence. Forexample, the mutation can randomly select two nucleotide bases andchange them to two other nucleotide bases.

The system can process the new mutated sequence using the neural networkto generate another output that characterizes how strongly the newmutated sequence binds to a particular target molecule (step 404), e.g.,as described above with reference to FIG. 3 .

The system uses the neural network to determine whether the outputindicates that the new mutated sequence binds stronger than the currentmutated sequence (step 406). That is, the system can compare a highthroughput sequencing count for a particular SELEX round of the currentmutated sequence with that of the new mutated sequence. If the highthroughput sequencing count for the new mutated sequence is higher thanthat of the current mutated sequence, the system determines the newmutated sequence binds more strongly to the target molecule than thecurrent mutated sequence. Otherwise, the system determines the currentmutated sequence binds more strongly to the target molecule than the newmutated sequence. In some implementations, the system factors in highthroughput sequencing counts between the current mutated sequence andthe new mutated sequence across multiple SELEX rounds. By way ofillustration, the system can compute weighted sums of high throughputsequencing counts across multiple SELEX rounds for each sequence todetermine which sequence more strongly binds to the target molecule.

The system selects another mutation to be applied in a next iterationbased on whether the output indicates that the new mutated sequencebinds stronger than the current mutated sequence (step 408). That is, ifthe new mutated sequence binds more strongly than the current mutatedsequence, the system randomly mutates the new mutated sequence andrepeats the process described above. Otherwise, the system randomlymutates the current mutated sequence and repeats the process describedabove. In some implementations, the system mutates the current mutatedsequence to a sequence not previously processed. The system can continuemutating sequences until the system generates a threshold number of newmutated sequences or if a higher binding strength is not found for athreshold number of iterations.

The system can repeat the process for a predetermined number of times.Alternatively, the system can repeat the process until the systemiterates on the current mutated sequence for a threshold number oftimes. In other words, the system stops repeating the process when thesystem is unable to generate, within the threshold number of times, anew mutated sequence that binds more strongly than the current mutatedsequence.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, in tangibly-embodied computer software or firmware, incomputer hardware, including the structures disclosed in thisspecification and their structural equivalents, or in combinations ofone or more of them. Embodiments of the subject matter described in thisspecification can be implemented as one or more computer programs, i.e.,one or more modules of computer program instructions encoded on atangible non-transitory program carrier for execution by, or to controlthe operation of, data processing apparatus. Alternatively or inaddition, the program instructions can be encoded on anartificially-generated propagated signal, e.g., a machine-generatedelectrical, optical, or electromagnetic signal, that is generated toencode information for transmission to suitable receiver apparatus forexecution by a data processing apparatus. The computer storage mediumcan be a machine-readable storage device, a machine-readable storagesubstrate, a random or serial access memory device, or a combination ofone or more of them. The computer storage medium is not, however, apropagated signal.

The term “data processing apparatus” encompasses all kinds of apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include special purpose logic circuitry, e.g., an FPGA(field programmable gate array) or an ASIC (application-specificintegrated circuit). The apparatus can also include, in addition tohardware, code that creates an execution environment for the computerprogram in question, e.g., code that constitutes processor firmware, aprotocol stack, a database management system, an operating system, or acombination of one or more of them.

A computer program (which may also be referred to or described as aprogram, software, a software application, a module, a software module,a script, or code) can be written in any form of programming language,including compiled or interpreted languages, or declarative orprocedural languages, and it can be deployed in any form, including as astand-alone program or as a module, component, subroutine, or other unitsuitable for use in a computing environment. A computer program may, butneed not, correspond to a file in a file system. A program can be storedin a portion of a file that holds other programs or data, e.g., one ormore scripts stored in a markup language document, in a single filededicated to the program in question, or in multiple coordinated files,e.g., files that store one or more modules, sub-programs, or portions ofcode. A computer program can be deployed to be executed on one computeror on multiple computers that are located at one site or distributedacross multiple sites and interconnected by a communication network.

As used in this specification, an “engine,” or “software engine,” refersto a software implemented input/output system that provides an outputthat is different from the input. An engine can be an encoded block offunctionality, such as a library, a platform, a software development kit(“SDK”), or an object. Each engine can be implemented on any appropriatetype of computing device, e.g., servers, mobile phones, tabletcomputers, notebook computers, music players, e-book readers, laptop ordesktop computers, PDAs, smart phones, or other stationary or portabledevices, that includes one or more processors and computer readablemedia. Additionally, two or more of the engines may be implemented onthe same computing device, or on different computing devices.

The processes and logic flows described in this specification can beperformed by one or more programmable computers executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application-specific integrated circuit).

Computers suitable for the execution of a computer program include, byway of example, can be based on general or special purposemicroprocessors or both, or any other kind of central processing unit.Generally, a central processing unit will receive instructions and datafrom a read-only memory or a random access memory or both. The essentialelements of a computer are a central processing unit for performing orexecuting instructions and one or more memory devices for storinginstructions and data. Generally, a computer will also include, or beoperatively coupled to receive data from or transfer data to, or both,one or more mass storage devices for storing data, e.g., magnetic,magneto-optical disks, or optical disks. However, a computer need nothave such devices. Moreover, a computer can be embedded in anotherdevice, e.g., a mobile telephone, a personal digital assistant (PDA), amobile audio or video player, a game console, a Global PositioningSystem (GPS) receiver, or a portable storage device, e.g., a universalserial bus (USB) flash drive, to name just a few.

Computer-readable media suitable for storing computer programinstructions and data include all forms of non-volatile memory, mediaand memory devices, including by way of example semiconductor memorydevices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks,e.g., internal hard disks or removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks. The processor and the memory can besupplemented by, or incorporated in, special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a computerhaving a display device, e.g., a CRT (cathode ray tube) monitor, an LCD(liquid crystal display) monitor, or an OLED display, for displayinginformation to the user, as well as input devices for providing input tothe computer, e.g., a keyboard, a mouse, or a presence sensitive displayor other surface. Other kinds of devices can be used to provide forinteraction with a user as well; for example, feedback provided to theuser can be any form of sensory feedback, e.g., visual feedback,auditory feedback, or tactile feedback; and input from the user can bereceived in any form, including acoustic, speech, or tactile input. Inaddition, a computer can interact with a user by sending resources toand receiving resources from a device that is used by the user; forexample, by sending web pages to a web browser on a user's client devicein response to requests received from the web browser.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., as a data server, or that includes a middleware component, e.g.,an application server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

While this specification contains many specific implementation details,these should not be construed as limitations on the scope of anyinvention or of what may be claimed, but rather as descriptions offeatures that may be specific to particular embodiments of particularinventions. Certain features that are described in this specification inthe context of separate embodiments can also be implemented incombination in a single embodiment. Conversely, various features thatare described in the context of a single embodiment can also beimplemented in multiple embodiments separately or in any suitablesubcombination.

Moreover, although features may be described above as acting in certaincombinations and even initially claimed as such, one or more featuresfrom a claimed combination can in some cases be excised from thecombination, and the claimed combination may be directed to asubcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particularorder, this should not be understood as requiring that such operationsbe performed in the particular order shown or in sequential order, orthat all illustrated operations be performed, to achieve desirableresults. In certain circumstances, multitasking and parallel processingmay be advantageous. Moreover, the separation of various system modulesand components in the embodiments described above should not beunderstood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

Particular embodiments of the subject matter have been described. Otherembodiments are within the scope of the following claims. For example,the actions recited in the claims can be performed in a different orderand still achieve desirable results. As one example, the processesdepicted in the accompanying figures do not necessarily require theparticular order shown, or sequential order, to achieve desirableresults. In certain implementations, multitasking and parallelprocessing may be advantageous.

What is claimed is:
 1. (canceled)
 2. A computer-implemented method fortraining a neural network to model results of an iterative SELEXprocess, wherein the iterative SELEX process begins with an initial poolof aptamers and iteratively updates the pool of aptamers at each of aplurality of different rounds, the method comprising: obtaining resultsof the iterative SELEX process applied to the initial pool of aptamers;obtaining data defining a plurality of sequences for a plurality ofaptamers in the initial pool of aptamers, each aptamer from theplurality of aptamers comprising a string of nucleobases; generating,based on the results of the iterative SELEX process, a respective labelfor each sequence from the plurality of sequences that comprisescharacteristics of how strongly the respective aptamer binds to at leastone target molecule, wherein the respective label for each sequence fromthe plurality of sequences includes a plurality of sub-labels and eachsub-label corresponds to a different round in the iterative SELEXprocess and characterizes a result of the corresponding round for therespective aptamer; generating training data for a neural network fromthe data defining the plurality of sequences and the respective labelsfor each of the sequences; and training the neural network on thetraining data, the neural network configured to generate, for aparticular aptamer, a model output that comprises a plurality neuralnetwork outputs, each neural network output corresponding to a differentround in the iterative SELEX process, and, wherein: each neural networkoutput is a prediction of a result of the corresponding SELEX round, ifthe particular aptamer were in the initial pool of aptamers as of thebeginning of the corresponding SELEX round, and the neural networkoutput corresponding to a first SELEX round in the iterative SELEXprocess comprises a count, a binary value or a confidence valueindicating whether the particular aptamer would exist in the pool ofaptamers at completion of the first SELEX round.
 3. The method of claim2, wherein the neural network output corresponding to the first SELEXround in the iterative SELEX process comprises the binary value or theconfidence value indicating whether the particular aptamer would existin the pool of aptamers at completion of the first SELEX round.
 4. Themethod of claim 2, wherein the count is a numerical value thatrepresents a predicted count of the particular aptamer in the pool ofaptamers for the first SELEX round.
 5. The method of claim 2, whereinthe model output comprises a vector of fluorescence levels, where abrightness of each fluorescence level indicates how strongly theparticular aptamer binds to the at least one target molecule.
 6. Themethod of claim 2, wherein the neural network is a convolutional neuralnetwork.
 7. The method of claim 2, wherein the data defining theplurality of sequences comprises, for each aptamer from the plurality ofaptamers in the initial pool, data specifying secondary structure of therespective aptamer.
 8. The method of claim 7, wherein the dataspecifying the secondary structure comprises data specifying a patternof hydrogen bonds in the respective aptamer or an energy dot plot matrixof the respective aptamer.
 9. The method of claim 2, wherein the neuralnetwork is configured process, for the particular aptamer, an encodingof data defining a sequence for the particular aptamer, the encodingcomprising a plurality of one-hot vectors, each one-hot vectorcorresponding to a respective nucleobase in the particular aptamer. 10.The method of claim 2, wherein the first SELEX round is before a secondSELEX round in the iterative SELEX process.
 11. A system comprising oneor more computers and one or more storage devices storing instructionsthat when executed by one or more computers cause the one or morecomputers to perform operations for training a neural network to modelresults of an iterative SELEX process, wherein the iterative SELEXprocess begins with an initial pool of aptamers and iteratively updatesthe pool of aptamers at each of a plurality of different rounds, theoperations comprising: obtaining results of the iterative SELEX processapplied to the initial pool of aptamers; obtaining data defining aplurality of sequences for a plurality of aptamers in the initial poolof aptamers, each aptamer from the plurality of aptamers comprising astring of nucleobases; generating, based on the results of the iterativeSELEX process, a respective label for each sequence from the pluralityof sequences that comprises characteristics of how strongly therespective aptamer binds to at least one target molecule, wherein therespective label for each sequence from the plurality of sequencesincludes a plurality of sub-labels and each sub-label corresponds to adifferent round in the iterative SELEX process and characterizes aresult of the corresponding round for the respective aptamer; generatingtraining data for a neural network from the data defining the pluralityof sequences and the respective labels for each of the sequences; andtraining the neural network on the training data, the neural networkconfigured to generate, for a particular aptamer, a model output thatcomprises a plurality neural network outputs, each neural network outputcorresponding to a different round in the iterative SELEX process, and,wherein: each neural network output is a prediction of a result of thecorresponding SELEX round, if the particular aptamer were in the initialpool of aptamers as of the beginning of the corresponding SELEX round,and the neural network output corresponding to a first SELEX round inthe iterative SELEX process comprises a count, a binary value or aconfidence value indicating whether the particular aptamer would existin the pool of aptamers at completion of the first SELEX round.
 12. Thesystem of claim 11, wherein the neural network output corresponding tothe first SELEX round in the iterative SELEX process comprises thebinary value or the confidence value indicating whether the particularaptamer would exist in the pool of aptamers at completion of the firstSELEX round.
 13. The system of claim 11, wherein the count is anumerical value that represents a predicted count of the particularaptamer in the pool of aptamers for the first SELEX round.
 14. Thesystem of claim 11, wherein the model output comprises a vector offluorescence levels, where a brightness of each fluorescence levelindicates how strongly the particular aptamer binds to the at least onetarget molecule.
 15. The system of claim 11, wherein the neural networkis a convolutional neural network.
 16. The system of claim 11, whereinthe data defining the plurality of sequences comprises, for each aptamerfrom the plurality of aptamers in the initial pool, data specifyingsecondary structure of the respective aptamer.
 17. The system of claim16, wherein the data specifying the secondary structure comprises dataspecifying a pattern of hydrogen bonds in the respective aptamer or anenergy dot plot matrix of the respective aptamer.
 18. The system ofclaim 11, wherein the neural network is configured process, for theparticular aptamer, an encoding of data defining a sequence for theparticular aptamer, the encoding comprising a plurality of one-hotvectors, each one-hot vector corresponding to a respective nucleobase inthe particular aptamer.
 19. The system of claim 11, wherein the firstSELEX round is before a second SELEX round in the iterative SELEXprocess.
 20. A system comprising: one or more computers; and one or morestorage devices storing instructions, which, when executed by the one ormore computers, cause the one or more computers to perform operationsfor modeling, for a particular aptamer and at least one target molecule,the results of an iterative SELEX process that begins with an initialpool of aptamers and iteratively updates the initial pool of aptamers ateach of a plurality of rounds, the operations comprising: obtaining datadefining a sequence for the particular aptamer, the aptamer comprising astring of nucleobases; encoding the data defining the sequence for theparticular aptamer as a neural network input; and processing the neuralnetwork input using a trained neural network to generate an output thatcomprises a plurality of neural network outputs, wherein: each neuralnetwork output corresponds to a different one of the plurality of roundsin the iterative SELEX process, and each neural network output is aprediction of a result of the corresponding SELEX round, if theparticular aptamer were in the initial pool of aptamers as of thebeginning of the corresponding SELEX round.
 21. The system of claim 20,wherein the neural network output corresponding to a first SELEX roundin the iterative SELEX process comprises a binary value or a confidencevalue indicating whether the particular aptamer would exist in the poolof aptamers at completion of the first SELEX round.
 22. The system ofclaim 21, wherein the neural network output corresponding to a secondSELEX round in the iterative SELEX process comprises a numerical valuethat represents predicted count of the particular aptamer in the pool ofaptamers for the second SELEX round, and the second SELEX round is afterthe first SELEX round in the iterative SELEX process.
 23. The system ofclaim 20, wherein the output comprises a vector of fluorescence levels,where a brightness of each fluorescence level indicates how strongly theparticular aptamer binds to the at least one target molecule.
 24. Thesystem of claim 20, wherein the neural network is a convolutional neuralnetwork.
 25. The system of claim 20, wherein the data defining thesequence comprises data specifying secondary structure of the particularaptamer.
 26. The system of claim 25, wherein the data specifying thesecondary structure comprises data specifying a pattern of hydrogenbonds in the particular aptamer or an energy dot plot matrix of theparticular aptamer.
 27. The system of claim 20, wherein encoding thedata defining the sequence comprises converting the data defining thesequence to a plurality of one-hot vectors, each one-hot vectorcorresponding to a respective nucleobase in the particular aptamer. 28.One or more non-transitory computer-readable media storing instructions,which, when executed by one or more computers, cause the one or morecomputers to perform operations for modeling, for a particular aptamerand at least one target molecule, the results of an iterative SELEXprocess that begins with an initial pool of aptamers and iterativelyupdates the initial pool of aptamers at each of a plurality of rounds,the operations comprising: obtaining data defining a sequence for theparticular aptamer, the aptamer comprising a string of nucleobases;encoding the data defining the sequence for the particular aptamer as aneural network input; and processing the neural network input using atrained neural network to generate an output that comprises a pluralityof neural network outputs, wherein: each neural network outputcorresponds to a different one of the plurality of rounds in theiterative SELEX process, and each neural network output is a predictionof a result of the corresponding SELEX round, if the particular aptamerwere in the initial pool of aptamers as of the beginning of thecorresponding SELEX round.
 29. The non-transitory computer-readablemedia of claim 28, wherein the neural network output corresponding to afirst SELEX round in the iterative SELEX process comprises a binaryvalue or a confidence value indicating whether the particular aptamerwould exist in the pool of aptamers at completion of the first SELEXround.
 30. The non-transitory computer-readable media of claim 29,wherein the neural network output corresponding to a second SELEX roundin the iterative SELEX process comprises a numerical value thatrepresents predicted count of the particular aptamer in the pool ofaptamers for the second SELEX round, and the second SELEX round is afterthe first SELEX round in the iterative SELEX process.
 31. One or morenon-transitory computer-readable storage media storing instructions thatwhen executed by one or more computers cause the one or more computersto perform operations for training a neural network to model results ofan iterative SELEX process, wherein the iterative SELEX process beginswith an initial pool of aptamers and iteratively updates the pool ofaptamers at each of a plurality of different rounds, the operationscomprising: obtaining results of the iterative SELEX process applied tothe initial pool of aptamers; obtaining data defining a plurality ofsequences for a plurality of aptamers in the initial pool of aptamers,each aptamer from the plurality of aptamers comprising a string ofnucleobases; generating, based on the results of the iterative SELEXprocess, a respective label for each sequence from the plurality ofsequences that comprises characteristics of how strongly the respectiveaptamer binds to at least one target molecule, wherein the respectivelabel for each sequence from the plurality of sequences includes aplurality of sub-labels and each sub-label corresponds to a differentround in the iterative SELEX process and characterizes a result of thecorresponding round for the respective aptamer; generating training datafor a neural network from the data defining the plurality of sequencesand the respective labels for each of the sequences; and training theneural network on the training data, the neural network configured togenerate, for a particular aptamer, a model output that comprises aplurality neural network outputs, each neural network outputcorresponding to a different round in the iterative SELEX process, and,wherein: each neural network output is a prediction of a result of thecorresponding SELEX round, if the particular aptamer were in the initialpool of aptamers as of the beginning of the corresponding SELEX round,and the neural network output corresponding to a first SELEX round inthe iterative SELEX process comprises a count, a binary value or aconfidence value indicating whether the particular aptamer would existin the pool of aptamers at completion of the first SELEX round.
 32. Theone or more non-transitory computer-readable storage media of claim 31,wherein the neural network output corresponding to the first SELEX roundin the iterative SELEX process comprises the binary value or theconfidence value indicating whether the particular aptamer would existin the pool of aptamers at completion of the first SELEX round.